Can I compile python code to make it faster?
Yes, you can compile Python code to make it faster, although the improvements in performance may vary depending on the specific use case. Compiling Python code can be done in several ways, including:

1. **Cython**: *Cython* is a language that is a superset of Python, allowing you to write Python-like code with optional static typing and direct C-level API access. *Cython* code can be compiled into C, which can then be compiled into a shared library or an extension module that can be imported into Python. This can significantly speed up performance-critical sections of your code.

2. **Nuitka**: *Nuitka* is a Python compiler that translates Python code into C++ code, which can then be compiled into an executable or a shared library. This can result in performance improvements, although the degree of improvement can vary.

3. **PyPy**: *PyPy* is an alternative Python interpreter with a Just-In-Time (JIT) compiler. While it doesn't compile your Python code ahead of time, it can provide significant speed improvements for many use cases when compared to the standard CPython interpreter. *PyPy* is usually as simple as switching the interpreter, and no code modification is required.

4. **Nuitka/Shedskin**: *Shedskin* is an experimental Python-to-C++ compiler that automatically generates C++ code from Python code. It only supports a subset of the Python language, and not all Python libraries are compatible with it. It is less mature than *Cython* and *Nuitka* but can provide significant speedups in some cases.

In the end the performance improvements will depend on the specific code you are working with, therefore you should measure the actual performance gains before and after using a compiler since most of these tools may require additional setup and adjustments to your code to work effectively.
Python is an interpreted language and Python binaries compiled for your target machine not only execute your Python code but also the interpreter that takes your code and interprets each command at run-time. That additional overhead of interpretation is what affects performance. However, there are ways to speed up your Python code without compiling it. One way is to use Python’s built-in functions which are one of the best ways to speed up your code. Another way is to use PyPy which is a faster implementation of Python that uses Just-In-Time (JIT) compilation. 

There is also a new "high-performance Python compiler that compiles Python code to native machine code without any runtime overhead," called Codon. Typical speedups over Python are on the order of 10-100x or more, on a single thread. Codon's performance is typically on par with (and sometimes better than) that of C/C++.